Systems and methods for workforce optimization and integration

ABSTRACT

Systems and methods are disclosed for an optimizing operations at a contact center. In one embodiment, an integrated contact center comprises: a workforce manager comprising a scheduler and a tracking function; and a lesson assignment function configured to receive at least one indicator of performance of the agent, and further configured to assign a lesson to the agent based on the at least one indicator.

FIELD OF THE DISCLOSURE

The present disclosure relates to workforce optimization of contactcenters.

BACKGROUND

The business of a call center, also known as a contact center, is toprovide rapid and efficient interaction between agents and customers (orprospective customers). Existing solutions require the purchase ofmultiple hardware and software components, typically from differentvendors, to achieve the business goals of the contact center. The use ofseparate systems of components leads to a variety of problems. Forinstance, each system typically has its own method of configuration andits own user interface. Thus, exchanging data between the systemsrequires additional work by someone at the contact center.

Furthermore, contact centers are continually tasked with striking abalance between service quality, efficiency, effectiveness, revenuegeneration, cost cutting, and profitability. As a result, today'scontact center agents are charged with mastering multiple data sourcesand systems, delivering consistent service across customer touch points,up-selling, cross-selling, and saving at-risk customers, while winningnew ones.

SUMMARY OF THE INVENTION

Systems and methods are disclosed for an optimizing operations at acontact center. In one embodiment, an integrated contact centercomprises: a workforce manager comprising a scheduler and a trackingfunction; and a lesson assignment function configured to receive atleast one indicator of performance of the agent, and further configuredto assign a lesson to the agent based on the at least one indicator.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with referenceto the following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present disclosure.

FIG. 1 is a block diagram of a contact center environment.

FIG. 2 is a diagram of one embodiment of the integrated integratedprocess for optimizing operations at a contact center.

FIG. 3 is a high-level view of components in one embodiment of anintegrated contact center system.

FIG. 4 shows a point of integration between the work force manager (WFM)and the quality monitor of FIG. 3.

FIG. 5 shows another point of integration between the work force manager(WFM) and the quality monitor of FIG. 3.

FIG. 6 shows several points of integration between the WFM and thelearning component of FIG. 3.

FIG. 7 shows several points of integration between the performancemanager and the learning component of FIG. 3.

FIG. 8 shows a point of integration between the WFM and the performancemanager of FIG. 3.

FIG. 9 shows another point of integration between the WFM and theperformance manager of FIG. 3.

FIG. 10 shows components of the analytics function of FIG. 3.

FIG. 11 is a hardware block diagram of a general-purpose computer thatcan be used to implement one or more of the components of the integratedcontact center systems, processes or methods.

DETAILED DESCRIPTION

The systems and methods described herein provide integrated solutionsfor performing workforce management, quality monitoring, e-learning,performance management, and analytics functionality. Combining qualitymonitoring/call recording with performance management and e-learningfunctionality as a unified integrated solution, delivered through asingle platform, enables users to gain more insight and make smarterdecisions faster about sales, service, and overall operations. Thistakes contact center tools beyond the traditional “suite” approach to atrue single workforce optimization platform.

The present invention represents a convergence of five contact centertechnology segments that work together in support of a greater customerservice strategy. In short, the integrated workforce optimizationplatforms disclosed herein integrate: (1) Quality Monitoring/CallRecording—voice of the customer; the complete customer experience acrossmultimedia touch points; (2) Workforce Management—strategic forecastingand scheduling that drives efficiency and adherence, aids in planning,and helps facilitate optimum staffing and service levels; (3)Performance Management—key performance indicators (KPIs) and scorecardsthat analyze and help identify synergies, opportunities and improvementareas; (4) e-Learning—training, new information and protocoldisseminated to staff, leveraging best practice customer interactionsand delivering learning to support development; and/or (5)Analytics—deliver insights from customer interactions to drive businessperformance These five segments can become part of an interwoven andinteroperable solution, enabling contact centers to transition fromreactive cost centers to proactive, information-rich departments thatdeliver strategic value to the organization. These four segments becomepart of an interwoven and basic integrated solution, enabling contactcenters to transition from reactive cost centers to proactive,information-rich departments that deliver strategic value.

Further, the integrated workforce optimization platforms disclosedherein provide closed-loop systems for continuous performanceimprovement, enabling contact centers to: establish realistic forecastsand performance goals; schedule and deploy the right number of staffwith the appropriate skills; capture customer interactions in theirentirety by recording all calls, or recording based on business rules,or on-demand, or randomly; measure performance to identify executionissues and excellence; analyze customer interactions to investigateopportunities for optimizing use of people, processes and technologies;take action by delivering targeted training or re-engineering processes;and/or refine forecasts and performance goals based on the collecteddata.

One embodiment of the integrated process and system disclosed hereinbegins with planning and establishing goals—from both an enterprise andcenter perspective—to ensure alignment and objectives that complementand support one another. Next comes forecasting and scheduling of theworkforce to ensure optimum service levels. Then recording and measuringperformance are utilized, leveraging quality monitoring/call recordingto assess service quality and the customer experience.

Next, the process/system analyzes and identifies opportunities andcorrelates them the with contact center or organization's KPIs andscorecards. Then, e-learning and company-specific “best practices”(documented through captured customer interactions) make it possible toaddress skill and knowledge gaps efficiently and effectively—as well asquickly communicate policy or procedural changes across thecenter—enabling the contact center to achieve success in whatever termsit chooses to define. Rather than arbitrarily sending e-learningtraining segments and hoping agents use them, contact centers can useadvanced workforce management forecasting and scheduling to select thebest time to administer training (which is proven to be more effectivethan classroom or group learning) as well as free supervisors to workone-on-one with agents.

Quality monitoring scores, including insights from analytics and/oranalytical analysis of structured, unstructured, or aggregated data, cannext be fed into a workforce management to produce staffing models thatprevent companies from unknowingly scheduling one shift with all the topperformers, for example. As a result, some embodiments of the workforcemanagement component of the process/system of the present invention canprovide a higher level of consistent service across shifts.

As can be seen, while each technology segment delivers value,integration is the key: together the segments deliver greater impactthan the sum of their individual parts. Utilizing them separately limitsthe contact center's potential to become a strategic business asset.

The integrated systems for workforce optimization disclosed hereinpotentially solve many deficiencies in today's maturing contact centerindustry. For instance, at an operational level, centers are focused onoptimizing customer sales/service representative (CSR) performance. Inthe process, they may be working under constraints, such as cost controland infrastructures that provides only bare essentials. They may alsoface the challenge of matching demand with resources, retainingeffective agents, prioritizing coaching/training, and deliveringconsistent customer experiences. Leveraging an integrated system and itscomponents, such as forecasting and scheduling, voice/screencapture/recording, evaluations and best practice training, enables themto focus on reducing risk, decreasing average handle time, improvingquality scores, driving down average time to answer, ensuring adherenceand managing occupancy.

At a more advanced level, contact centers are focused on optimizingcontact center performance. They face the challenge of balancingproductivity with quality, increasing center-driven revenue,standardizing service across touch points, and growing transactioncomplexities. Contact centers are examining such metrics as first callresolution, shrinkage, up-selling and cross-selling, and customersatisfaction as driven though the contact center. As disclosed herein,the forecasting and scheduling, adherence, business rules-drivenrecording, lesson management, and agent/organizational scorecardfunctionality—for example—unites contact center experiences, providesflexible scheduling, and promotes the initiation of a performanceimprovement culture.

The subject matter disclosed herein is related to the subject matterdisclosed in several pending U.S. patent applications. One is entitled“Enterprise Manager for Recorders from a Central Point ofAdministration,” Attorney Docket No. 762301-1180, filed Feb. 22, 2006,and entirely incorporated by reference herein. The subject matter of the1180 application is centralized administration of voice, video, and datarecorders, and enabling role-based access control of recorders which donot have role-based security concepts.

Another is “Systems and Methods for Scheduling Call Center Agents usingQuality Data,” Attorney Docket No. 762301-1280, filed Feb. 22, 2006, andentirely incorporated by reference herein.

Another is “Systems and Methods for Scheduling Call Center Agents usingQuality Data,” Attorney Docket No. 762301-1280, filed Feb. 22, 2006, andentirely incorporated by reference herein.

Another is “Systems and Methods for Scheduling Call Center Agents usingQuality Data and Correlation-Based Discovery,” Attorney Docket No.762301-1010, filed Feb. 22, 2006, and entirely incorporated by referenceherein.

Another is “System and Method for Integrating Learning Systems andScorecards Systems”, Attorney Docket No. 762301-1090, filed Feb. 22,2006, and entirely incorporated by reference herein.

Another is “System and Method for Integrating Learning Systems andWorkforce Management Systems”, Attorney Docket No. 762301-1150, filedFeb. 22, 2006, and entirely incorporated by reference herein.

Another is U.S. application Ser. No. 10/136,705, entitled “Method andSystem for Presenting Events Associated with Recorded Data Exchangedbetween a Server and a User,” and entirely incorporated by referenceherein. The subject matter of the '705 application includes capturingand graphically displaying events that occur during an interactionbetween a customer and an agent. A reviewer is presented with asummarized voice interaction session, in the form of a call timeline,including a list of event identifiers. The reviewer selects one of theevent identifiers in the timeline, and the interaction session, startingwith the selected event, is presented to the user. The user could chooseto start listening to the exchange at an event by selecting the event.

Another is U.S. application Ser. No. 10/137,480, entitled “Method andSystem for Selectively Dedicating Resources for Recording Data Exchangedbetween Entities Attached to a Network,” filed on Apr. 30, 2002, andentirely incorporated by reference herein. The subject matter of the'480 application includes determining whether to use an active tap or apassive tap to record data passing through a particular node based uponan objective for recording as noted by predefined business rules.

Another is U.S. Ser. No. 10/136,735, entitled “Methods and Systems forCategorizing and Cataloguing Recorded Interactions,” filed on Apr. 30,2002, and entirely incorporated by reference herein. The subject matterof the '735 application includes categorizing data upon storing thecaptured data. The categories are based upon predefined business rulesfor storing captured data.

Another is U.S. application Ser. No. 10/061,469, entitled “Method,Apparatus, and System for Capturing Data Exchanged between a Server anda User,” filed on Jan. 31, 2002, and entirely incorporated by referenceherein. The subject matter of the '469 application includes capture ofexchange data by a capture module that operates independently from theserver and the user.

Another is U.S. application Ser. No. 10/061,489, entitled “Method,Apparatus, and System for Processing Data Captured during Exchangesbetween a Server and a User,” filed on Jan. 31, 2002, and entirelyincorporated by reference herein. The subject matter of the '489application includes selective recordation of captured data based uponwhether the data satisfies predetermined business rules.

Another is U.S. application Ser. No. 10/061,491, entitled “Method,apparatus, and system for replaying data selected from among datacaptured during exchanges between a server and a user,” filed on Jan.31, 2002, and entirely incorporated by reference herein. The subjectmatter of the '491 application includes replaying data captured during asession, wherein search criteria are based upon business rules.

The following is a list of other U.S. utility applications which includerelated subject matter, each of which is enclosed by reference: U.S.utility application, entitled, “Method and Apparatus for Long-RangePlanning,” having U.S. Ser. No. 09/899,895, filed Oct. 3, 2002; U.S.utility application entitled, “Interface System and Method of BuildingRules and Constraints For a Resource Scheduling System,” having U.S.Ser. No. 09/680,131, filed Oct. 2, 2000; U.S. Utility Applicationentitled, “System and Method for Complex Schedule Generation,” havingU.S. Ser. No. 09/825,589, filed Apr. 3, 2001; U.S. utility applicationentitled, “Method and Apparatus for Long-Range Planning,” having U.S.Ser. No. 09/899,895, filed Jul. 5, 2001; U.S. utility applicationentitled, “Method and Apparatus for Multi-Contact Scheduling,” havingU.S. Ser. No. 11/037,604, filed Jan. 18, 2005; and U.S. Utilityapplication entitled, “Method and Apparatus for Concurrent ErrorIdentification in Resource Scheduling,” having U.S. Ser. No. 11/237,456,filed Sep. 27, 2005.

Contact Center Environment

FIG. 1 is a block diagram of a contact center environment 100. Thecontact center 100 is staffed by agents who handle incoming and/oroutgoing contacts. Although the traditional and most common form ofcontact is by phone, other types of contacts are becoming more common(e.g., text chat, web collaboration, email, and fax). An agent workspaceincludes an agent phone 110 and a workstation computer 120. A network130 connects one or more of the workstations 120.

A call router 140 distributes incoming contacts to available agents.When the contacts are made by traditional phone lines, the call router140 operates by connecting outside trunk lines 150 to agent trunk lines160. In this environment, the call router 140 may be implemented by anautomatic call distributor (ACD), which queues calls until a suitableagent is available. Other types of contacts, such as Voice over InternetProtocol (VoIP) calls and computer-based contacts (e.g., chat, email)are routed over one or more data networks. These contacts aredistributed over network 130 to one of the agent workstations 120.

During a customer contact, the agent interacts with one or moreapplications running on the workstation 120. Example workstationapplications give the agent access to customer records, productinformation, ordering status, and transaction history, for example.

The business purpose of a contact center is to provide rapid andefficient interaction between agents and customers. To achieve thispurpose, a contact center follows a business process having stages, inthat one stage affects subsequent stages.

In a conventional contact center business process, there is a relativelyhigh degree of separation between stages. In contrast, in the integratedcontact center business process 200 (FIG. 2) described here, multiplestages are connected into a loop, with each stage of the process feedinginput into another stage down the line.

First Embodiment

FIG. 2 is a diagram of one embodiment of the integrated integratedprocess for optimizing operations at a contact center (200), in whichseveral interfaced organizations are combined as a single integratedoperational process and/or platform. In the first stage (210), thebusiness goals of the contact center are defined. Goals are defined interms of metrics that describe how the contact center is expected toperform. Some metrics relate to expected revenue, such as revenue/houror revenue/agent. Other metrics relate to service level, such astime-to-answer and rate of first-call resolution. Persons familiar withcontact center operations will understand these and many other businessgoals and metrics.

The first stage (210) may also include campaign planning. Profiles forcampaigns are defined, for example by: inbound or outbound; how manycontacts are expected; date and duration of the campaign; and what sortsof agent skills are needed.

Information about the goals and campaign(s) produced by the first stage(210) is provided to the second stage (220). In the second stage (220),a workforce of agents is scheduled to staff the campaign(s). Indetermining the number of agents scheduled for a campaign, thegoals/metrics and campaign characteristics from the first stage (210)are considered. The schedule also uses as input a workload forecast,which predicts contact volume during each interval of the campaign,based on historical data. Using this schedule, the contact centermanager deploys the appropriate number and mix of agents during thecampaign times.

The output of the second stage (220) is the customer-agent interactionsthat occur during a campaign. The third stage (230) measures or assessesthe interactions in various ways. One typical assessment (“adherence”)measures how well an agent complied with contact center policies (e.g.,call duration). In the third stage (230), at least a portion of theinteractions are recorded and then examined. This examination produces avariety of quality metrics that assess an agent's skills in variouscategories (product knowledge, selling, listening, etc.)

The various assessments are provided as input to the fourth stage (240).In this stage, these inputs are analyzed in various ways. The analysismay rate interactions on a “good” to “bad” scale, considering thecustomer point of view, the business point-of-view, or both. Forexample, a contact that resulted in a sale would be an indicator of a“good” interaction while a contact that exceeded average duration wouldbe an indicator of a “bad” interaction.

Once “bad” interactions are identified, an attempt is made to determinea root cause. In some cases, the root cause may lie with an agent (e.g.,weak product skills). In other cases, the cause may be in the contactcenter infrastructure or operations (e.g., customer database is slow).The cause might also be rooted in a business process of the enterprisethat is sponsoring the campaign. For example, the billing process usedby the enterprise, or the process by which the enterprise dispatchesfield service units could be the cause.

The fifth stage (250) uses the analysis produced by the fourth stage(230) to adapt and change operations accordingly. Agent skills can beimproved by training in the deficient areas. The information may be usedto change an aspect of contact center operations, or to make arecommendation to the sponsoring enterprise for it to change itsprocesses or operations. The results of the analysis, as well as the rawmetrics used as input to the analysis, are combined into data sets(“scorecards”) that allow the contact center operators to determinewhether or not the business goals are met and whether the metrics showprogress toward the goals or away from the goal (“trending”). These datasets are provided as input to the first stage (210), which closes thefeedback loop of the integrated contact center business process 200.

Second Embodiment

FIG. 3 is a high-level view of components in one embodiment of anintegrated contact center system 300. The integrated system 300 includestwo or more of the following component: a work force manager (WFM) 310;a quality monitoring component 320; a learning component 330; and aperformance management component 340. These components (310-340)cooperate to implement the integrated contact center business process(200) as described earlier.

As will be described, combining agent quality metrics from the qualitymonitor 320 (e.g., synchronous such as voice, asynchronous such ase-mail or chat) with WFM 320 (e.g., agent planning, scheduling) mayprovide insight that contact center supervisors can use to confirm thevalue provided by agents to the business as a whole.

The WFM 310 performs many functions related to the agent workforce. Forexample WFM 310 can: schedule single, multiple, or virtual contactcenters across multiple time zones; accommodate a dedicated, blended, ortask-switching environment; schedule meetings or training without impacton service levels; allow agents to bid for shifts and provide input intotheir schedules; automate compliance with government and unionregulations; create centralized forecasts and schedules with a singlepoint of control over the entire network, or decentralized schedulesthat allow for decision-making at individual sites; schedule based onskill priorities that align with the contact center's routing strategy;and create and schedule teams as a unit to support training andaccommodate employee preferences.

The functionality of the entire WFM 310 is typically divided amongseveral applications, executables, processes, or services. A forecastand scheduling component (350) calculates staffing levels and agentschedules based on historical interaction (contact) patterns. A trackingcomponent (355) provides a contact center supervisor or manager withinformation about agent activities and agent-customer interactions, bothhistorical and real-time. An adherence component (360) supplies thesupervisor with information on how well each agent complies with callcenter policies. For example, once schedules are created, the contactcenter should ensure that agents follow the schedules.

Most preferably, the adherence component 360 provides a real-time viewof every activity across each channel in the contact center, includingthose in the front and back office, so supervisors/contact centers cansee how their staff spends its time. In an enhancement, alerts can beset to notify supervisors when agents are out-of-adherence and exceptionmanagement can help insure agents are correctly recognized for work theyhave performed.

The quality monitor 320 includes a content recorder (370) for recordingagent-customer interactions. The content recorder 370 can be configuredto capture all interactions, or a selected set of interactions based onuser-defined business rules.

The content recorder 370 can capture voice and data interactions fromboth traditional and IP telephony environments and can handlehigh-volume recording for compliance and sales verification. The contentrecorder 370 can also record all voice transactions across multiplesites, or randomly capture a subset of transactions that may be ofparticular interest, as well as record contacts on-demand. Using thecontent recorder 370 a user can record all contacts or establishadvanced business rules to capture only those transactions of particularinterest. User-defined business rules can trigger the recordings,initiate enterprise collaboration by notifying individuals or groups ofthe captured contacts and emerging trends, and allow users to assignattributes or “tags” to the contacts for quick identification. All datarelated to a customer interaction—including navigation of automatedsystems, agent keystrokes and desktop activities—can be storedautomatically in folders for search and retrieval. Different users in anenterprise can share and review transactions, as well as hear customerfeedback first-hand.

The quality manager 320 stores the interactions in an interactionsdatabase 375, which may include descriptive information as well asrecorded content. Contact center personnel play back some of theinteractions and use an evaluation component (380) to score the agent invarious categories (product knowledge, selling, listening, etc.)

Furthermore, contact center supervisors and quality analysts can thentap into these recorded interactions to review, evaluate, and scoreagent performance. An analytics component (385) can analyze interactionsin various ways, including the use of speech analytics. Examples ofanalysis include categorizing calls based on content, analyzing a callagainst an expected call pattern and reporting exceptions to thepattern, and providing a visualization layer for recorded interactionsthat displays other data attributes such as agent activities coincidentwith call events.

The learning component 330 allows a contact center manager to developtraining lessons for agents and assign lessons to agents. The learningcomponent 330 provides automated training processes by identifying,scheduling, and delivering online learning directly to agent desktops.The lesson content can include recorded interactions, which can be usedto create a library of best practices for training agents and otherpersonnel. Using actual interactions, a contact center can developE-learning content specific to the organization. In an enhancement,these training lessons can include assessments to help track and measureagent performance, skill acquisition, and knowledge retention.

The learning component 330 can also deliver targeted learning sessionsover a network, using e-mail, or a hyperlink to a Web site, or directlyto the agent desktop. Supervisors can select the appropriate trainingsessions from a library of courseware or create sessions themselvesusing a contact editing feature. Then supervisors can assign coursematerial and monitor completion automatically.

The performance manager 340 displays key performance indicators (KPIs),which can be predefined on a scorecard. The scorecard, which can berole-appropriate, provides a statistical measure of how well an agent orgroup of agents is performing (against their goals). The KPI metrics arederived from quality evaluations and/or WFM call routing data.

A centralized administration component (390 consolidates agentadministration across the various components into a single point ofentry, and provides a single logon to all components for agents andadministrators. The administration component 390 may also include acentralized reporting component, even across multiple sites. Aconsistent user interface (395) reduces training time on the varioussystem components.

An integrated contact center system such as system 300 allows contactcenter analysis to quickly access the right information. Such anintegrated system allows valuable and previously undiscoveredinformation to be uncovered. This new level of visibility into contactcenter operations should allow personnel make better decisions faster.

Third Embodiment

FIG. 4 shows a point of integration between two components of theintegrated contact center system 300, the WFM 310 and the qualitymonitor 320. Conventional call center systems provide and “interactions”application that allows playback of recorded interactions and livemonitoring of interactions. Importantly, these conventional systems didnot integrate interactions with WFM adherence information. Theintegration between the WFM 310 and the quality monitor 320 described inFIG. 4 allows a supervisor to “drill down” and examine a particularrecorded interaction from a display of agent activity and/or adherenceinformation.

In this disclosure, the term “interaction” refers to a record of thecontent of agent activities related to a call. Note that agentactivities are not limited to audio of the call itself. Other forms ofmedia are included. Examples of other types of interactions are: videorecording of the agent; application activity on the agent's workstation120; web pages delivered to the agent and/or customer duringcollaborative sessions; messages delivered through e-mail, instantmessaging, or other messaging technologies. Also, the agent activitiesin an interaction are not limited to the duration of the call, but canoccur after the call (a state called “wrap up” or “research”).

The tracking component 355 of the WFM 310 provides information aboutagent activities to the WFM adherence component 360. Agent activities,which describe work activities performed by agents, are collected fromvarious sources. The call router 140 (FIG. 1) reports agent call states(Available, After-Call-Work, etc.) An application monitor on agentworkstations 120 tracks agent activity on the workstation (e.g.,switching between applications, screen data, keyboard input, etc.).

The adherence component 360 displays a view (410) of agent activities,typically one agent per line, with activities arranged across atimeline. Exceptions to agent adherence (e.g., non-compliance withcontact center policy) are displayed in conjunction with the activitiesand the timeline.

The adherence component 360 obtains a list (420) of recordedinteractions available for agents during the displayed time period. Thislist of interactions is presented to the user in the same adherence view(410).

From this adherence view, a user can “drill down” to a recordedinteraction by selecting (430) the interaction from the list, and thenactivating a playback tool. The adherence component 360 retrieves (440)the selected interaction from the interactions database 375, and theinteraction is then played back using an appropriate application (e.g.media player, desktop activity player, web content player). A user canalso select an agent activity that is presently occurring and eitherrecord on demand (450) or live monitor (460) the selected activity.

Integration between the WFM 310 and the quality monitor 320 is furtherdescribed in the U.S. patent application “System and Method forIntegrated Display of Recorded Interactions and Call Agent Data,”Attorney Docket Number 762301-1160, filed the same day and by the sameassignee as the instant application.

Fourth Embodiment

FIG. 5 shows an additional point of integration between the WFM 310 andthe quality monitor 320, in which agent activity, adherence, and/orscheduling information is used to trigger selective recording in aselective recording environment, or to perform smart selection ofrecording for evaluation in a total recording environment. In aconventional quality monitor 320, the content recorder 370 can beconfigured to record a certain number, or percentage, of agent-customerinteractions. This parameter is typically fixed for the duration of acampaign, though it can vary from one campaign to the next.

In the integrated system 500, the WFM 310 generates call recordingparameters 510 based on information contained in the forecast 520 (e.g.,call volume and call type) and/or the schedule 530. The recordingparameters 510 are provided to the content recorder 370 in the qualitymonitor 320. This integration allows the content recorder 370 to adaptrecording behavior during a campaign.

As an example of how this feature is useful to a contact center,consider a marketing campaign that starts on a Monday and lasts allweek. It is expected that call quality for agents on this campaign willbe relatively low on Monday, since the material is new to the agents. Bythe end of the week, the agents are more familiar with the material, sothat agent quality scores are expected to increase.

The recording parameters 510 provided to the content recorder 370 in theintegrated systems 500 allow a contact center manager to increase thepercentage of interactions recorded at the start of the campaign, and toreduce the percentage as the campaign progresses. recording parameters510 can be further associated with one agent, or a set of agents, sothat inexperienced agents (e.g., agents with low scores) have a higherpercentage of recorded interactions as compared to more experiencedagents.

Other examples of using WFM data to determine recording behaviorinclude: trigger or select recording based on relative elapsed time fromthe beginning of the shift; trigger or select recording before or afterspecific activities (e.g., after lunch or before break activity); andtrigger select recording based on adherence data (e.g., agent is on callbut not adhering to schedule).

Fifth Embodiment

FIG. 6 shows several points of integration between the WFM 310 and thelearning component 330. The learning component 330 includes lessons 610.Each lesson 610 is designed to improve an agent's competence in aparticular area. Lessons are assigned, either manually or automatically,through a lesson assignment component 620, which communicatesinformation about the assignment (630) to the scheduler 350 in the WFM310. In one embodiment, the information 630 includes an agentidentifier, a lesson identifier, a lesson duration, and a lessoncompletion date. After receiving the lesson assignment information 630,the scheduler 350 modifies the schedule 530 to include a trainingactivity for the identified agent. If possible, the new trainingactivity is scheduled before the lesson completion date.

An agent receives training through a lesson presentation function 640.The presentation may take the form of viewing a video and/or listeningto audio on the agent workstation 120. The lesson presentation function640 maintains a lesson log 650 which tracks the presentation of lessons610 to agents. In one implementation the lesson log 650 includes anagent identifier, a lesson identifier, the time when the lessonpresentation began, and an indication of whether the lesson has beencompleted.

In yet another point of integration between WFM 310 and the learningcomponent 330, the lesson log 650 is provided to the adherence component360 in the WFM 310. The adherence component 360 uses information in thelesson log 650 to determine whether an agent has met the lesioncompletion date. If not, the adherence component 360 notes theincomplete lesson as an exception to adherence.

Scheduling assigned lessons and tracking adherence to these assignmentsis further described in the U.S. patent application “Tracking of LessonAdherence in a Call Center Environment,” Attorney Docket Number762301-1150, filed the same day and by the same assignee as the instantapplication.

Sixth Embodiment

FIG. 7 shows several points of integration between the performancemanager 340 and the learning component 330. The performance manager 340maintains more key performance (KPIs) 710 that measure how well an agentor group of agents is performing. The KPIs 710 may be based one or moresource measurements 720, such as evaluations from the quality monitor320 and call statistics from call router 140 (e.g., call duration, holdtime during call, etc.)

The performance manager 340 does analysis on the KPIs 710 and/or thesource measurements 720 to produce scorecards 730. The analysis mayinclude calculating statistics such as average, variation, etc.,aggregating by time period or groups of agents, and determining trends.The scorecards 730 are then presented in visual form to a user. Examplesof scorecard are a daily scorecard for an agent or a team, and ascorecard of all agents for the past month.

In the integrated system 700, the KPIs 710 are also provided (740) tothe learning component 330. As described earlier, the learning component330 maintains lessons 610 which can be assigned to an agent for review.In the integrated system 700, each lesson 610 is associated with one ormore KPIs 710. The lesson assignment component 620 examines one or moreof the KPIs 710 for a particular agent, and makes an assignment (750)for a lesson 610 associated with that KPI 710, based on criteriaassociated with a KPI or a competency. In one implementation, thecriteria is a comparison of one or more KPIs 710 for an agent tothreshold values, and the lesson assignment component 620 assigns alesson 610 if the KPI 710 is lower than the threshold. This point ofintegration therefore allows automatic lesson assignment based on KPI710.

Automatic lesson assignment is further described in the U.S. patentapplication “Integration of E-Learning and Scorecards in Call CenterOperation,” Attorney Docket Number 762301-1090, filed the same day andby the same assignee as the instant application.

The presentation may also include a test that is given to the agent todetermine competency the area associated with the lesson 610. In yetanother point of integration between WFM 310 and the learning component330, the agent test score 760 for an agent is provided to theperformance manager 340. The performance manager 340 updates the KPIs710 to reflect the agent competency described by the test score 760.

Seventh Embodiment

FIG. 8 shows a point of integration between the WFM 310 and theperformance manager 340. Conventional schedulers allow agents to setpreferences for shift assignments (e.g., one agent prefers to workweekends and another prefers to work nights). Since most agents areexpected to prefer a day shift rather than a midnight shift, shiftpreferences are typically combined with agent ranking or seniority, sothat someone works the midnight shift. This leads to a situation wherethe midnight shift is staffed with all of the “worst” agents.

As described earlier, the performance manager 340 maintains KPIs 710that measure agent and/or group performance. In the integrated system800 shown in FIG. 8, the scheduler 350 considers agent KPIs 710 whenscheduling, so that some “good” agents are also added to the shift. TheKPI 710 may reflect, for example, an evaluation of the agent'sperformance on a set of customer interactions. In one embodiment, thescoring is done by a human while playing back the recorded interaction.In another embodiment, the scoring is at least partly automated throughthe use of speech analytics.

The agent KPIs 710 are provided to the scheduler 350 in the WFM 310.Also provided to the scheduler 350 are quality goals 810 for aparticular schedule interval. Examples of quality goals are “50% ofagents have a score at of least 80” and “average score is at least 80.”

The scheduler 350 considers the quality goals 810 and the KPIs 710,along with other inputs, to determine a schedule 530 which includesagent assignments to work activities at specific times. The scheduler350 also considers other inputs, such as a workload forecast 820, agentskill sets 830 and agent shift preferences 840. The scheduler 350 thenchooses a mix of agents to work a shift, so that the agent scorescombine to meet the quality goals 810. Integration of KPIs and thescheduler is further described in the U.S. patent application “Systemsand Methods for Scheduling Call Center Agents Using Quality Data,”Attorney Docket Number 762301-1010, filed the same day and by the sameassignee as the instant application.

Eighth Embodiment

FIG. 9 shows another point of integration between the WFM 310 and theperformance manager 340. As described earlier, the performance manager340 maintains KPIs 710 that measure agent and/or group performance, andproduces scorecards 730 from the KPIs 710. The scorecards 730 provide aquick way for a manager to determine areas that require attention. Forexample, if a particular agent is out of adherence or has a lowcompetency score, then the adherence or competency KPI can be flaggedwith a warning icon. Typically, the manager wants more detailedinformation about the flagged problem area. A conventional contactcenter solution requires the manager to open up a particularapplication, such as Adherence or Quality Monitoring, to obtain detailedinformation about the problem area. Once in the application, the managermust then navigate to the root cause of the problem (e.g., the activitythat was out of adherence).

In contrast, the integrated system 900 allows a user to quickly viewdetails associated with the flagged problem area, in the appropriateapplication context. Several examples of this use of application contextare shown in FIG. 9. When interacting with the performance manager 340,selecting an adherence-related KPI (910) in a scorecard 730 brings theuser to a view (920) of adherence information. Furthermore, theparticular agent activities that resulted in the out-of-adherence flag910 are highlighted or otherwise brought to the user's attention in theview 920. As another example, selecting a quality score-related KPI(930) brings the user to the quality monitor 320, and more specificallyto the particular evaluation form 940 which contains the flagged qualityscore 930.

As yet another example, selecting a call statistic-related KPI (950),such as call duration or hold time, brings the user to the qualitymonitor 320. The quality monitor 320 presents a list of recordedinteractions (from the interactions database 375) which contributed to,or are in someway related to, the flagged call-statistic score 950. Theuser can then play back (960) one of the recorded interactions. Theintegrated system 900 thus greatly simplifies root cause analysis forcontact center personnel.

Ninth Embodiment

Call recording and monitoring are vital to contact center operations andthe business. Every day, insight and feedback on the organization aregained from customer interaction. Valuable business intelligence can beextracted from these calls to help call center executives improveoperational efficiency, customer satisfaction, and profitability. Yetmanagement can only listen to a small segment of recorded calls.Managers must search manually through an enormous number of calls justto find the calls they need to analyze. The process is not onlyinefficient and expensive, but valuable information is continuallyignored, leaving only a small sample of data needed to make informedbusiness decisions.

Referring now to FIG. 10, with the analytics function 385 of the presentinvention (first introduced in FIG. 3), contact centers can now convertall call recordings into actionable business intelligence. Managementcan discern important competitive and business insight and easilyidentify trends from customer interactions, by analyzing speech,telephony, agent, and recording data together. In an enhancement, theanalytics function 385 also streamlines the quality monitoring processby automatically classifying and scoring calls, based on selectioncriteria that may include any or part of the data captured by theintegrated systems disclosed herein, including speech analytics.

The analytics function 385 of the present invention enables businessesto: (1) have a more accurate view of the customer experience, whichallows executives across the organization uncover critical customerattitudes, needs, and requirements; (2) automatically score and classifycalls for easy retrieval and examination, which enables call centers todigitally score calls to conduct automated quality and customersatisfaction surveys; and (3) discover trends related to customerbehavior (e.g. churn, product adoption)that impact the business.

The analytics function 385 preferably uses speech recognition 1000 toconvert the recorded calls into a searchable repository that allows forthe query of words and/or phrases contained within the recorded calls.This repository may manifest itself as a text transcript or searchablephonetic model of the recorded calls. The analytics function 385 mayapply additional unstructured data analysis techniques to refine andextract the context and further meaning from the conversations. Examplesof various techniques that may be applied to refine the context of themined speech, or the speech-to-text conversion, include: statisticalmodeling of grammar using a statistical model of grammar 1010 module;and natural language processing using a natural speech patterns 1020module. Further, the analytics function 385 identifies the criticalwords and phrases within the context of the conversation. All thisenables the embodiments disclosed herein to capture the intent of thecall, rather than merely the words of the call.

In an alternate embodiment, the analytics function 385 converts theaudio of the conversation into a phonetic representation of the call anduses a word-spotting method 1030 (or a query analysis), which flags ortags calls by a specific word, phrase, proximity, inflection, tempo, oremotion. Queries may be performed on an ad-hoc basis or stored forpattern analysis.

With the recorded calls converted to searchable content (via atranscribe call 1040 represented in FIG. 10), the analytics function 385allows users to look back in time to discover what customers have said.In the preferred embodiment, users do not need to know in advance whatthey are looking for. For example, if there were a spike in call volumelast week, the analytics function 385 can enable the contact center tounderstand the reason for the increased calls. Also, the user canincorporate metadata obtained from telephony or CRM systems to gainfurther insight into the reasons for the call spike.

In an enhancement, the analytics function 385 also uses a patternrecognition module 1050 to pull meaning out of the results generated byspeech recognition. The pattern recognition module 1050 discerns thecall's pattern and automatically places the call into one or severalcategories once the call is ingested into the speech engine, based oncontext the pattern recognition module 1050 is able to extract from thespeech mining function. The patterns are used not only to classify callsbut also to determine if a particular activity has occurred during thecall, or to automatically score individual evaluation or surveyquestions based on this data. For instance a call score might becorrelated to an existing evaluation or customer survey question duringthe call (e.g., “did the agent offer a cross sell”, “did the agentremember to read the corporate policy”). By automating thelabor-intensive quality monitoring processes, contact centers canrealize not just a fast return on investment, but also deploy resourceswhere they are strategic to the call center.

The analytics function 385 can link the call content to the metadatafrom, for example, a quality monitoring component (see FIG. 3), torelate characteristics such as agent ID, time/date, speaker's name,workgroup ID, and call routing. The analytics function 385 can link tocustom data sources that may contain other information related to theagent/customer interaction, for example, a CRM system.

The analytics function 385 also includes a search function 1060. Anappend feature in the search function allows the user to modify theinitial search by tacking on additional criteria and logic. A refinefeature function allows the user to add to the search criteria, whichare then used on the results of the last search. A remove feature allowsthe user to modify the initial search by tacking on additional criteriaand logic. An undo allows any of the modifications just described to bereversed. In one enhancement, results from the initial search stringusing the search function 1060 can be refined to help focus onparticular calls of interest. In another enhancement, users can combinethe search functionality described above with data from the CTI, ACD andother sources via a CTI ACD integration 1070 module.

Different individuals use different words or phrases to depict a similarmeaning. Recognizing this fact, the analytics function 385 enables usersto expand single words into complete concepts, which convey intent andmeaning, rather than being tied to one narrow possibility. An expansionoption 1080 allows users to include plural, synonym, homonym, andcontaining words, in a single clean screen. For example, instead ofsearching for the single word “bill”, the user can select to search for“bill, bills, account, charges, invoice, statement, billing, billed,bell”, which will most likely return better results because it takesinto account the differences of expression.

In one enhancement, the expansion option 1080 allows for theidentification of temporal relationships between words, phrases andother collected events in order to better identify the context of theconversation. For example, a search that includes the word “supervisor”in a temporal relationship with words like “transfer me to”, or inrelationship to a call transfer event, can provide much more contextthan a search for “supervisor”. The expansion option allows users tocapture more instances of the concept that they are exploring andfurthermore establish the intent of the calls. This improves uponkeyword-spotting technologies, which are not good enough to performad-hoc searching for concepts, which is the ultimate goal in contentdiscovery.

The analytics function 385 further enables the user a variety of ways toderive insight from the search results. The Call Replay 1090 componentallows the user to listen to an audio file from the search results, inpart or its entirety. Playing a portion of the audio allows the usermore efficiently go through the search results without having to wastetime listening to the whole conversation. The Text Display 1092component shows a continuous text for the entire recognized content whenplaying back part or all of a call. This allows users quickly captureterms and expressions exchanged in the call that might be of importance.The Save Searches 1094 component allows a user to save and easilyretrieve searches for further refinement and analysis. The Export 1096component allows search results to be exported to a wide variety offormats, such as Microsoft Excel or Adobe PDF format. The SearchStatistics 1098 component displays information on the current search(e.g. calls counted, search time). In one enhancement, the analyticsfunction 385 further includes call visualization component whichincludes an interface for displaying the text of a set of calls alongwith other data captured by the integrated system of the presentinvention along with integrated sources. A call visualization componentis more fully described in the '705 application and incorporated byreference above).

Preferably, the analytics function 385 automatically classifies andscores calls via classify calls 1062 module and a score calls 1064module. This feature can greatly reduce the time and effort that contactcenters spend on the quality monitoring process by “structuring”unstructured voice recordings and categorizing them. The classify calls1062 module preferably classifies calls based on the content. A call maybe classified into one or more “buckets.” The analytics function 385relies on the concept that all conversational threads have at their coreone or more “patterns” of speech.

Patterns are complex descriptions of different ways that peoplecommunicate information, not just simple “words” for matching. Thesepatterns of speech do not have to contain exact word matches forparticular search terms, but they only “look” like a specific pattern.Each pattern is defined and assigned a weight by the pattern developer,and each area of intent is assigned a threshold. If a group of patternsmatch and their added weights exceed the threshold, then thatconversation is said to “look” like and contain that intent.

The weights and threshold are user definable and therefore easilytweaked to produce better and more accurate results. A typical intent“bucket” will contain anywhere from five to 100 “patterns” to match.Patterns can be shared across domains and industries, and pattern basescan evolve forward to deliver ever more accurate and finely tunedpattern matching.

The analytics function 385 uses patterns not only to classify calls viathe classify calls 1062 module, but also to evaluate if a particularactivity occurred during a call via the score calls 1064 module. Theuser begins by designating the objective criteria on which the calls areto be scored into the application. A set of patterns is then describedfor the criteria. A call is then scored based on the extent to which thecriteria patterns were fully met, partially met, or not met at all. Eachweighted threshold for each score level can be customizable.

The analytics function 385 allows the user to create a graphicalrepresentation of trends found in the calls via a graphicalrepresentation 1066 module. This enables a user to view statistics aboutcomplex trends over a large time period.

The trend view displays a suite of ad-hoc reports that can be easilyconfigured by the parameters in Table 1. TABLE 1 Time/Day Interval Valueto Calculate Segmentation Day of Week Avg # Words Per Call By AgentMonth Avg Call Length In Seconds By Agent Group By Week Call Count ByContent Group By Quarter Hit Total By Customer Account By Year Sum(WAVLength) By Department By Location

By visualizing the information by the parameters above, the user cangain a more detailed view on the particularities of the search phrases.

Another trending capability is the display of, for example, the top 200words mentioned in the recorded calls (where the number of top words iscustomizable). The analytics function 385 proactively shows the wordsthat are unusually more frequent than before or compared to the standardlanguage. This acts as an “early warning system” to enable organizationsto understand how the conversations have changed from one period to thenext.

Preferably, the analytics function 385 organizes and delivers resultscustomized to the end-users requirements via a reports 1068 module. Inan enhancement, reports 1068 module allows for scheduling options thatenable users the ability to vary frequency of report delivery soanalysts can zoom in on critical data metrics hourly, daily, monthly,etc. Users can customize and automate reporting. Once a query iscreated, the user can save the query to run automatically. Users cancreate and view reports in different formats while using the web-basedviewer. For example, reports can be output as Excel or PDF files, andthen emailed. The reports are interactive, in that calls can be playedback live from the results of the report. The reports 1068 module, whichis preferably based on industry-standard databases such as SQL, can beused to customize reports, to extract, format and report from theunderlying data. In another enhancement, the reports 1068 module is adashboard reporting system which can, for example, link the actual callsdetected for each event or report.

General Purpose Computer

FIG. 11 is a hardware block diagram of a general-purpose computer 1100that can be used to implement one or more of the components of theintegrated contact center system 300 disclosed herein, or the integratedcontact center processes or methods disclosed herein. The computer 1100contains a number of components that are well known in the art of callcenter software, including a processor 1110, a network interface 1120,memory 1130, and non-volatile storage 1140. Examples of non-volatilestorage include, for example, a hard disk, flash RAM, flash ROM, EEPROM,etc. These components are coupled via a bus 1150. The memory 1130contains instructions which, when executed by the processor 1110,implement the methods and systems disclosed herein. Omitted from FIG. 11are a number of conventional components, known to those skilled in theart that are unnecessary to explain the operation of the system 1100.

The systems and methods disclosed herein can be implemented in software,hardware, or a combination thereof. In some embodiments, the systemand/or method is implemented in software that is stored in a memory andthat is executed by a suitable microprocessor (μP) situated in acomputing device. However, the systems and methods can be embodied inany computer-readable medium for use by or in connection with aninstruction execution system, apparatus, or device. Such instructionexecution systems include any computer-based system,processor-containing system, or other system that can fetch and executethe instructions from the instruction execution system. In the contextof this disclosure, a “computer-readable medium” can be any means thatcan contain, store, communicate, propagate, or transport the program foruse by, or in connection with, the instruction execution system. Thecomputer readable medium can be, for example but not limited to, asystem or propagation medium that is based on electronic, magnetic,optical, electromagnetic, infrared, or semiconductor technology.

Specific examples of a computer-readable medium using electronictechnology would include (but are not limited to) the following: anelectrical connection (electronic) having one or more wires; a randomaccess memory (RAM); a read-only memory (ROM); an erasable programmableread-only memory (EPROM or Flash memory). A specific example usingmagnetic technology includes (but is not limited to) a portable computerdiskette. Specific examples using optical technology include (but arenot limited to) optical fiber and compact disc read-only memory(CD-ROM).

Note that the computer-readable medium could even be paper or anothersuitable medium on which the program is printed. Using such a medium,the program can be electronically captured (using, for instance, opticalscanning of the paper or other medium), compiled, interpreted orotherwise processed in a suitable manner, and then stored in a computermemory. In addition, the scope of the certain embodiments of the presentinvention includes embodying the functionality of the preferredembodiments of the present invention in logic embodied in hardware orsoftware-configured mediums.

It should be noted that any process descriptions or blocks in flowchartsshould be understood as representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. As would beunderstood by those of ordinary skill in the art of the softwaredevelopment, alternate embodiments are also included within the scope ofthe disclosure. In these alternate embodiments, functions may beexecuted out of order from that shown or discussed, includingsubstantially concurrently or in reverse order, depending on thefunctionality involved.

This description has been presented for purposes of illustration anddescription. It is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Obvious modifications orvariations are possible in light of the above teachings. The embodimentsdiscussed, however, were chosen to illustrate the principles of thedisclosure, and its practical application. The disclosure is thusintended to enable one of ordinary skill in the art to use thedisclosure, in various embodiments and with various modifications, asare suited to the particular use contemplated. All such modificationsand variation are within the scope of this disclosure, as determined bythe appended claims when interpreted in accordance with the breadth towhich they are fairly and legally entitled.

1. An integrated contact center comprising: a workforce managercomprising a scheduler; and a quality monitor configured to provide, tothe scheduler, at least one quality goal for a work period and at leastone agent quality score, wherein the scheduler is configured to producea workforce schedule comprising agent assignments to work activities,wherein the workforce schedule is based at least in part on the at leastone quality goal and the at least one agent quality score.
 2. The systemof claim 1, wherein the workforce schedule is based at least in part onagent skill sets and agent shift preferences.
 3. The system of claim 1,wherein the scheduler is further configured to select at least oneinteraction recording parameter based on a workload forecast input andto provide the recording parameter to the quality monitor.
 4. Anintegrated contact center comprising: a workforce manager comprising ascheduler and a tracking component; and a lesson assignment componentconfigured to receive at least one indicator of performance of an agent,and further configured to assign a lesson to the agent based on the atleast one indicator.
 5. The system of claim 4, wherein the scheduler isfurther configured to modify a workforce schedule to include a trainingwork activity based on lesson assignment data received from the lessonassignment function.
 6. The system of claim 4, wherein the scheduler isfurther configured to select a time for the training work activity basedat least in part on the lesson assignment data.
 7. The system of claim4, further comprising: an adherence component; and a lesson presentationfunction configured to provide, to the adherence component, a log oflessons presented to an agent.
 8. The system of claim 4, furthercomprising a lesson presentation function configured to present a lessonto an agent, and to test the agent on information presented in thelesson, and to provide a test score to the workforce manager.
 9. Anintegrated contact center comprising: a performance manager configuredto receive a plurality of performance indicators and to present theperformance indicators in a scorecard; and a workforce managercomprising an adherence viewer, wherein the performance manager isfurther configured to receive a selection of one of the performanceindicators, and to invoke an appropriate application based on theselected indicator.
 10. The system of claim 9, wherein performancemanager is further configured to the invoke the adherence viewer whenthe selected indicator is derived from adherence information.
 11. Thesystem of claim 10, wherein the adherence viewer is configured todisplay adherence information that is associated with the selectedindicator.
 12. The system of claim 9, wherein the adherence viewer isconfigured to invoke the quality manager when the selected indicator isderived from an agent quality score produced by the quality manager. 13.The system of claim 12, wherein the quality manager is configured, wheninvoked by the performance manager, to present an evaluation form usedby the quality manager to produce the agent quality score.
 14. Thesystem of claim 9, wherein the adherence viewer is configured to invokethe quality manager when the selected indicator is at least partlyderived from an interaction recorded by the quality manager.
 15. Thesystem of claim 14, wherein the quality manager is configured, wheninvoked by the quality manager, to play back the interaction from whichthe selected indicator is at least partly dervived.
 16. The system ofclaim 10, wherein the adherence viewer is further configured to display,in visual correlation with a timeline, agent interactions during aspecified time period.